I am reading a jpeg image (input.jpg) and on writing the same image on disk(as output.jpg), why does its size getting changed. Can i retain the same size.
library(jpeg)
img <- readJPEG('input.jpg')
file.info('input.jpg')$size
writeJPEG(img,'output.jpg',1)
file.info('output.jpg')$size
#is different from input.jpg size
Well, what's you doing is not reading and writing back the same file. readJPEG decodes compressed (lossy) JPEG data into raster array, writeJPEG encodes it back again. To get approximately the same size, you should (at least) set quality parameter to appropriate value. See ?writeJPEG:
writeJPEG(image, target = raw(), quality = 0.7, bg = "white", color.space)
There are a number of factors that affect the compression:
Sampling Rates
Quantization table selection
Huffman tables (optimal or canned)
Progressive or Sequential
If progressive, the breakdown of the scans.
Metadata included
If you do not compress using the same settings as the input, you will get a different size.
You can also get rounding errors in the decompression process that could cause slight differences.
Related
I'm converting images with embedded ICC profiles to the sRGB color space using LittleCMS. I want to efficiently detect when this conversion is lossy due to the image containing pixels that are out-of-gamut for the sRGB (e.g. the image is using Display P3 and contains saturated colors).
Note that I don't want to merely check if the embedded profile has a wide gamut, because the image pixels may not be taking advantage of its full gamut, and still fit in sRGB.
I know there's cmsSetAlarmCodes, but this seems like it could cause false positives, because there's nothing stopping the image from actually containing the same color that I set for my alarm color. Does LCMS have some out of band signal for this?
Another approach that comes to my mind is applying conversion twice: to sRGB and then back to the original color space, and checking how lossy it was. But that more than doubles processing time. Is there a more efficient way?
I have an array of bytes that represents the palette indexes of the pixels of an image, and I'm trying to convert this to an image with ImageSharp, so I can save it later as PNG, but I can't seem to find how, can anyone give me an idea on where to look? The palette is not important, I just need N different colors.
ImageSharp images represent the expanded pixel buffer, not references to color palettes. If you have the original palette use it to create the input buffer of actual pixel data. A byte array as described is not, in real terms a fully decoded image.
Reducing a color set to a maximum amount is a question of quantization. There are methods that allow you to do this as well as encoding options that allow saving images in indexed formats.
I've read lossless jpeg is invoked when the user selects a 100% quality factor in an image tool.
What is the image tool they meant?
Thanks in advance.
OK Image Compression is sorta like a ZIP file; JPG Takes up less space but has less quality then a PNG or TIFF. In short it removes Bytes and Changes The Algorithm, the Higher the Quality The More Space it Takes for the Algorithm Compression, Read More Here:
https://en.wikipedia.org/wiki/Lossless_compression
Suppose we have formed the codebook using the RGB training images. This codebook is now present at the encoder and the decoder.
Now we have one RGB test image (which is not contained in the training images) which we want to compress, transmit and reconstruct. During the reconstruction of this test image, due to the different intensities of the test image, some of which might completely not match with any training image intensity, wouldn't parts of the reconstructed image be darker or brighter than the original image in existing vector quantization algorithms? Is there any way deal with the intensities in existing algorithms like K-means, LBG? Or should we make an appropriate choice of the training images to begin with? Or should the test image be also included within the training images? What is the standard way?
Vector Quantization is a lossy compression scheme. You are finding the best-match clusters within the training set to create the codebook. It is an approximation. The larger the training set, the better the match will be, but there will always be loss.
Your training set needs to account for all intensities (complexities) of images, not only the intensity of the image you intend to compress. Whether or not the training images contain the test image won't change the fact that loss will occur (any gain will be insignificant, unless the training set is very small).
I have a image with size of 5760 x 3840px with size of 1.14mb. But I would like to reduce its size to 200 kb by not changing the dimensions of the image. How Could I do this using photoshop? Please help me
This answer is based on a large format file size that has a resolution of 300 dpi. If the image is 5760 x 3840, your canvas size will be 19.2 x 12.8. in order to not "reduce" the dimension (printable area) you are going o have to reduce the image size [ALT + CTRL/CMD + I] and then reduce the resolution from there.
At 300 DPI:
At 72 DPI:
This reduction in resolution can decrease the file size dramatically, There is a chance of artifacts, but as you are starting at a high resolution the compression algorithms will smooth it out to almost non-existent.
NOW... If you are starting at 72dpi and you are looking for a good way to generate a lower file size for the web, your best bet may be to do a Safe for web option [ALT + CTRL/CMD + SHIFT + S] and generat a .jpg, .gif or a .png based on the final needs of the file. If it is an photograph with a lot of colors, I would go .jpg. If you have a lot of areas of solid color (logo perhaps) I would go with .png or .gif.
The Save for Web option allows you to see, side by side, the results of the export BEFORE going through the save process. it also allows you to alter the settings of the save process to dial in your results. best of all, it gives you a pretty good preview of the expected file size.
Either way this should help you save that larger file size for future use.